Perception as Bayesian inference
Perception as Bayesian inference
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Probabilistic interpretation of population codes
Neural Computation
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
Learning in graphical models
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Computation in a single Neuron: Hodgkin and Huxley revisited
Neural Computation
Neural representation of probabilistic information
Neural Computation
Bayesian computation in recurrent neural circuits
Neural Computation
Neural Computation
Formal Tools for the Analysis of Brain-Like Structures and Dynamics
Creating Brain-Like Intelligence
Belief propagation in networks of spiking neurons
Neural Computation
Cortical circuitry implementing graphical models
Neural Computation
Reward-modulated hebbian learning of decision making
Neural Computation
Vectorized algorithms for spiking neural network simulation
Neural Computation
Computational Models of Learning the Raising-Control Distinction
Research on Language and Computation
Neurons as ideal change-point detectors
Journal of Computational Neuroscience
Continuous real-world inputs can open up alternative accelerator designs
Proceedings of the 40th Annual International Symposium on Computer Architecture
Attention as reward-driven optimization of sensory processing
Neural Computation
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We show that the dynamics of spiking neurons can be interpreted as a form of Bayesian inference in time. Neurons that optimally integrate evidence about events in the external world exhibit properties similar to leaky integrate-and-fire neurons with spike-dependent adaptation and maximally respond to fluctuations of their input. Spikes signal the occurrence of new information---what cannot be predicted from the past activity. As a result, firing statistics are close to Poisson, albeit providing a deterministic representation of probabilities.